MétaCan
Menu
← Back to cohort
Record W4409528600 · doi:10.7759/cureus.82452

Academic Output of Anesthesiology Departments in Canada From 2014 to 2023: A Bibliometric Analysis Study

2025· review· en· W4409528600 on OpenAlexaffabout
Ekambir Saran, Connor T. A. Brenna, Jiwon Lee, Ella Huszti, Shiven Sharma, Karim S. Ladha

Bibliographic record

VenueCureus · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineAnesthesiologyBibliometricsMedical educationLibrary scienceAnesthesia

Abstract

fetched live from OpenAlex

Anesthesia research is essential for advancing clinical practice and patient care. The purpose of this study was to analyze research productivity in Canadian anesthesiology departments from 2014 to 2023, focusing on trends in publication volume, methodology, and the impact of the COVID-19 pandemic. A bibliometric analysis was conducted following a pre-registered protocol to identify articles in the PubMed database, which were published between 2014 and 2023 (inclusive) with corresponding authors from Canadian anesthesiology departments. Data extracted for each article included the year of publication, journal, and study design. Descriptive statistics and Pearson correlation coefficient were used to compare trends, while annual publication rates were assessed with linear regression. An interaction term captured differences between pre-pandemic (2014-2020) and post-pandemic (2021-2023) periods. A total of 3,490 articles met the inclusion criteria. From 2014 to 2020 (pre-pandemic period), publication volume increased significantly by 28.7 studies/year (95% CI: 19.2-38.2, p < 0.001). In contrast, 2021-2023 (post-pandemic period) showed a non-significant decline of 13.0 studies/year (95% CI: -48.6-22.6, p = 0.405). Pre-pandemic trends showed significant growth in reviews, case-control/cohort studies, and surveys, while publication rates declined across most categories after 2020. Our findings illustrate an increase in research productivity among Canadian anesthesiology departments from 2014 to 2020, followed by a plateau in publication volume after the onset of the COVID-19 pandemic. This stagnation highlights a critical area for future exploration, including examining how pandemic-related factors, such as shifts in clinical priorities, resource allocation, and adoption of telemedicine in pre-operative clinics, have influenced research productivity. As the field of anesthesiology adapts to post-pandemic realities, ongoing bibliometric studies will be essential to monitor these trends and guide the trajectory of Canadian anesthesia research amid emerging clinical challenges and evolving academic priorities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.441

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.075
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.1200.231
Science and technology studies0.0030.001
Scholarly communication0.0080.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.371
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueCureus→Same topicCardiac, Anesthesia and Surgical Outcomes→French-language works237,207→